Mapping co-occurrence dynamics between crops and honeybees under climate change in North America
Bibliographic record
Abstract
Crop–pollinator interactions are vital for sustaining global food production, yet they face growing challenges from climate change. While the role of pollinators, particularly honeybees, in supporting crop yields is widely recognized, there is limited research on how these interactions might evolve under changing climatic conditions in North America. This study seeks to fill this knowledge gap by evaluating potential shifts in honeybee–crop interactions by 2070 under the SSP585 climate change scenario. The analysis identifies regions that are vulnerable to losing pollination services and those likely to experience enhanced interactions. Using species distribution models (SDMs), habitat suitability maps were developed for the western honeybee ( Apis mellifera ) and 23 pollinator-dependent crops under both current and future climate conditions. Co-occurrence probability maps were generated by integrating these data, enabling a detailed analysis of how interactions are likely to change across North America. The results indicate significant shifts in honeybee–crop interactions due to climate change. Northern regions, such as Canada, are expected to see increased interactions, driven by improved habitat suitability at higher latitudes. In contrast, southern and eastern areas, including parts of Mexico and the USA, are projected to experience declines, leaving these regions more susceptible to the loss of critical pollination services. These findings highlight the uneven effects of climate change on pollination services across North America. While northern areas may benefit from improved conditions, the anticipated declines in the south and east emphasize the urgent need for targeted conservation efforts and adaptive management strategies. Safeguarding pollination networks and ensuring agricultural sustainability will be crucial to mitigating these challenges and maintaining food security in the face of climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".